A hybrid multi-channel surface EMG decomposition approach by combining CKC and FCM

Author

Yong Ning ; Shanan Zhu ; Xiangjun Zhu ; Yingchun Zhang

Author_Institution

Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China

fYear

2013

fDate

6-8 Nov. 2013

Firstpage

335

Lastpage

338

Abstract

A hybrid approach is successfully developed in this study by combining the fuzzy C means (FCM) clustering method and Convolution Kernel Compensation (CKC) method for multi-channel surface electromyogram (EMG) decomposition. The FCM is utilized to estimate the initial innervation pulse trains (IPTs) of motor units (MUs) from a few channel surface EMG signals, the CKC method is then employed to estimate the final IPTs. Computer simulation results demonstrate the improved efficiency and accuracy of the hybrid approach compared to the classic CKC method.

Keywords

convolution; electromyography; medical signal processing; channel surface EMG signals; computer simulation; convolution kernel compensation method; fuzzy C means clustering method; hybrid multichannel surface EMG decomposition approach; initial innervation pulse trains; motor units; multichannel surface electromyogram decomposition; Convolution; Educational institutions; Electromyography; Kernel; Signal to noise ratio; Surface reconstruction;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on

Conference_Location

San Diego, CA

ISSN

1948-3546

Type

conf

DOI

10.1109/NER.2013.6695940

Filename

6695940